Showing papers in "Technological Forecasting and Social Change in 2021"
TL;DR: In this paper, the potential effects of the COVID-19 pandemic on the tourism industry were measured using panel structural vector auto-regression (PSVAR) on data from 1995 to 2019 in 185 countries and system dynamic modeling.
Abstract: Our paper is among the first to measure the potential effects of the COVID-19 pandemic on the tourism industry. Using panel structural vector auto-regression (PSVAR) (Pedroni, 2013) on data from 1995 to 2019 in 185 countries and system dynamic modeling (real-time data parameters connected to COVID-19), we estimate the impact of the pandemic crisis on the tourism industry worldwide. Past pandemic crises operated mostly through idiosyncratic shocks' channels, exposing domestic tourism sectors to large adverse shocks. Once domestic shocks perished (zero infection cases), inbound arrivals revived immediately. The COVID-19 pandemic, however, is different; and recovery of the tourism industry worldwide will take more time than the average expected recovery period of 10 months. Private and public policy support must be coordinated to assure capacity building and operational sustainability of the travel tourism sector during 2020–2021. COVID-19 proves that pandemic outbreaks have a much larger destructive impact on the travel and tourism industry than previous studies indicate. Tourism managers must carefully assess the effects of epidemics on business and develop new risk management methods to deal with the crisis. Furthermore, during 2020–2021, private and public policy support must be coordinated to sustain pre-COVID-19 operational levels of the tourism and travel sector.
TL;DR: The authors' findings indicate that the automobile industry perceived that the best strategies to mitigate risks related to COVID-19, were to develop localized supply sources and use advanced industry 4.0 (I4.0) technologies, and Big Data Analytics (BDA) to play a significant role by providing real-time information on various supply chain activities to overcome the challenges posed by CO VID-19.
Abstract: There has been an increased interest among scholars to investigate supply chain resilience (SCRes) in manufacturing and service operations during emerging situations. Grounded in the SCRes theory, this study provides insights into the impact of the COVID-19 outbreak on the automobile and airline supply chain. Both the short and long-term response strategies adopted by the two supply chains are assessed, using a combination of qualitative and quantitative techniques in three distinct phases. In phase one, we use a sequential mixed-method for resilience evaluation, integrating Time-to-Recovery (TTR) and Financial Impact (FI) analysis. In phase two, we conduct an empirical survey involving 145 firms to evaluate the short-term SCRes response strategies. In the third phase, we conduct semi-structured interviews with supply chain executives both from the automobile and airline industries to understand the long-term SCRes response strategies. Our findings indicate that: (i) the automobile industry perceived that the best strategies to mitigate risks related to COVID-19, were to develop localized supply sources and use advanced industry 4.0 (I4.0) technologies. (ii) The airline industry on the other hand, perceived that the immediate need was to get ready for business continuity challenges posed by COVID-19, by defining their operations both at the airports and within the flights. (iii) Importantly, both the sectors perceived Big Data Analytics (BDA) to play a significant role by providing real-time information on various supply chain activities to overcome the challenges posed by COVID-19. (iv) Cooperation among supply chain stakeholders is perceived, as needed to overcome the challenges of the pandemic, and to accelerate the use of digital technologies.
TL;DR: In this paper, the authors compared the two widely used methods of Structural Equation Modeling (SEM): covariance based CB-SEM and Partial Least Squares based SEM.
Abstract: This study compares the two widely used methods of Structural Equation Modeling (SEM): Covariance based Structural Equation Modeling (CB-SEM) and Partial Least Squares based Structural Equation Modeling (PLS-SEM). The first approach is based on covariance, and the second one is based on variance (partial least squares). It further assesses the difference between PLS and Consistent PLS algorithms. To assess the same, empirical data is used. Four hundred sixty-six respondents from India, Saudi Arabia, South Africa, the USA, and few other countries are considered. The structural model is tested with the help of both approaches. Findings indicate that the item loadings are usually higher in PLS-SEM than CB-SEM. The structural relationship is closer to CB-SEM if a consistent PLS algorithm is undertaken in PLS-SEM. It is also found that average variance extracted (AVE) and composite reliability (CR) values are higher in the PLS-SEM method, indicating better construct reliability and validity. CB-SEM is better in providing model fit indices, whereas PLS-SEM fit indices are still evolving. CB-SEM models are better for factor-based models like ours, whereas composite-based models provide excellent outcomes in PLS-SEM. This study contributes to the existing literature significantly by providing an empirical comparison of all the three methods for predictive research domains. The multi-national context makes the study relevant and replicable universally. We call for researchers to revisit the widely used SEM approaches, especially using appropriate SEM methods for factor-based and composite-based models.
TL;DR: In this article, the authors employed institutional theory and resource-based view theory to elucidate the way in which automotive firms configure tangible resources and workforce skills to drive technological enablement and improve sustainable manufacturing practices and furthermore develop circular economy capabilities.
Abstract: The significance of big data analytics-powered artificial intelligence has grown in recent years. The literature indicates that big data analytics-powered artificial intelligence has the ability to enhance supply chain performance, but there is limited research concerning the reasons for which firms engaging in manufacturing activities adopt big data analytics-powered artificial intelligence. To address this gap, our study employs institutional theory and resource-based view theory to elucidate the way in which automotive firms configure tangible resources and workforce skills to drive technological enablement and improve sustainable manufacturing practices and furthermore develop circular economy capabilities. We tested the research hypothesis using primary data collected from 219 automotive and allied manufacturing companies operating in South Africa. The contribution of this work lies in the statistical validation of the theoretical framework, which provides insight regarding the role of institutional pressures on resources and their effects on the adoption of big data analytics-powered artificial intelligence, and how this affects sustainable manufacturing and circular economy capabilities under the moderating effects of organizational flexibility and industry dynamism.
TL;DR: In this article, the authors investigated the effects of technological innovation within certain countries on the energy efficiency performance of neighboring countries, using data from the OECD Triadic Patent Families database for 24 innovating countries between the years 1994 and 2013.
Abstract: It is widely accepted that technological innovation reduces energy intensity and carbon emissions without compromising global economic growth. Although new innovative developments tend to be concentrated in a few developed countries, transboundary spillover of technological innovation influences the energy efficiency and sectoral performance of other countries. A more thorough assessment of international knowledge spillover related to energy intensity reduction can enhance understanding of mitigation opportunities and costs. This study investigated, therefore, the effects of technological innovation within certain countries on the energy efficiency performance of neighboring countries. We used data from the OECD Triadic Patent Families database for 24 innovating countries between the years 1994 and 2013. Accounting for geographical distance, our results showed a positive, significant relationship between knowledge spillover and country-specific energy efficiency performance. All countries showed a sustainable efficiency growth trend, which indicates a steady increase in energy efficiency. Germany, France, the UK, the Netherlands, and Switzerland are the most energy efficient countries. These results have policy implications for sustainable energy management and environmental sustainability, highlighting the need to develop domestic research and development capabilities that increase innovation-based infrastructure.
TL;DR: Technologies that relate directly to the treatment of the virus as well as those that have been used to adapt to living under this crisis are presented, highlighting how these technologies may prove helpful in the future.
Abstract: In contrast to earlier coronavirus diseases such as SARS or MERS, whose impact was largely limited to specific regions of the world, the novel coronavirus, COVID-19, is affecting people across the globe. This article analyzes the effects of this worldwide phenomenon on certain technologies and how this may improve our lives. It presents technologies that relate directly to the treatment of the virus as well as those that have been used to adapt to living under this crisis. Given that such a pandemic will likely affect humanity again, this article also highlights how these technologies may prove helpful in the future. To this end, technological challenges, related innovation logics, and their social impacts are discussed.
TL;DR: In this paper, the authors argue that the relationship between GIC, GHRM, and green innovation is more complex than previously suggested, and they suggest that environmental strategies are directly related to environmental performance.
Abstract: Extant literature suggests that green intellectual capital (GIC), green human resource management (GHRM), and green innovation (GI) impacts the environmental performance of firms. In this paper, we argue that the relationship between GIC, GHRM, GI and environmental performance is more complex than previously suggested. We propose that neither GIC nor GHRM are directly related to environmental performance. We argue instead that GI mediates the relationships between GIC, GHRM, and environmental performance. Further, we suggest that environmental strategies are directly related to environmental performance, while also moderating the relationship between GI and environmental performance. We tested our proposed model on a sample of 244 large manufacturing firms. The results of a structural equation modeling analysis provide support for most of our hypotheses.
TL;DR: In this paper, the authors examined the impact of technological innovation and fiscal decentralization on carbon dioxide (CO2) emissions in the presence of gross domestic product (GDP) and globalization in the case of China for the period 2005Q1 to 2018Q4.
Abstract: Deteriorating environmental quality poses a serious threat to life on earth. Similar to other countries, China has been attempting to reduce its reliance on non-renewable energy sources by adopting new energy-efficient technologies that help create a more sustainable industrial structure. Various studies have been conducted to determine the leading causes of environmental degradation. However, unlike international trade, economic activities, and eco-innovation, the political structure of a country is often ignored by scholars because of its indirect impact—which is difficult to evaluate—on emission reduction. In this study, we examine the impact of technological innovation and fiscal decentralization on carbon dioxide (CO2) emissions in the presence of gross domestic product (GDP) and globalization in the case of China for the period 2005Q1 to 2018Q4. By using time series econometric techniques, we find that technological innovation, fiscal decentralization, GDP, and globalization are influential factors in explaining CO2 emissions in China. In terms of policy implications, we suggest that to deal with deteriorating environmental quality, China needs to formulate policies to mitigate emission levels by promoting an energy-efficient system. Moreover, to smoothen the process, it is imperative to clarify the responsibilities at different levels of government to successfully achieve the targets of low CO2 emissions and energy-saving functions of fiscal expenditures.
TL;DR: In this paper, the authors investigate the relationship between financial risk and carbon emissions and explore the mediation effect of technological innovation on the financial risk-emission nexus. But, their results show that technological innovation and financial risk have a significant inhibitory effect on global carbon emissions only in the 10th quantile, while promoting carbon emissions in other quantiles.
Abstract: To empirically verify whether financial risk affects global carbon emissions, this study investigates the financial risk-emission nexus by employing a global balanced panel dataset of 62 countries over the period 2003–2018. Furthermore, we explore the mediation effect of technological innovation on the financial risk-emission nexus. Fully considering potential regional heterogeneity and asymmetry, this study further analyzes the heterogeneous and asymmetric relationships among the variables, such as the difference between regional comprehensive economic partnership countries and other countries. The empirical results indicate that: (1) a mediation effect between financial risk and global carbon emissions exists; in other words, increased financial risk not only reduces global carbon emissions directly, but can also have an indirect impact in mitigating carbon emissions by promoting technological innovation; (2) the impacts of financial risk and technological innovation on global carbon emissions show significant regional heterogeneity; and (3) financial risk and technological innovation show asymmetry across different quantiles. To be specific, technological innovation and financial risk have a significant inhibitory effect on global carbon emissions only in the 10th quantile, while promoting carbon emissions in other quantiles.
TL;DR: A framework is outlined showing the extent to which AI can replace humans and what is important to consider in making the transformation to the digital organization of innovation is explained.
Abstract: Artificial Intelligence (AI) reshapes companies and how innovation management is organized. Consistent with rapid technological development and the replacement of human organization, AI may indeed compel management to rethink a company's entire innovation process. In response, we review and explore the implications for future innovation management. Using ideas from the Carnegie School and the behavioral theory of the firm, we review the implications for innovation management of AI technologies and machine learning-based AI systems. We outline a framework showing the extent to which AI can replace humans and explain what is important to consider in making the transformation to the digital organization of innovation. We conclude our study by exploring directions for future research.
TL;DR: Wang et al. as mentioned in this paper provided preliminary evidence on the influence of China's carbon emission trading scheme pilot policy on green innovation based on green patent data and found that the pilot policy has an evident lagging effect on restraining the green innovation of enterprises.
Abstract: For climate change mitigation, China launched seven pilot areas before establishing a unified carbon emission trading system in 2014. This study explores the “weak” version of the Porter hypothesis while focusing on listed companies in 31 provinces (municipalities or autonomous regions) from 1990 to 2018. In this study, we provided preliminary evidence on the influence of China's carbon emission trading scheme pilot policy on green innovation based on green patent data. Results show that the “weak” Porter hypothesis has not been realized in the current carbon trading market of China. Moreover, the pilot policy has significantly decreased the proportion of green patents by approximately 9.26%. Then, we find that the pilot policy has an evident lagging effect on restraining the green innovation of enterprises. Furthermore, inhibition is more pronounced among the samples of small-scale, manufacturing, and non-state-owned companies, including companies in the eastern and central regions. Most importantly, companies mainly choose to reduce output rather than increase green technological innovation to achieve their emission reduction targets. Moreover, companies reduce their investment in research and development because of the reduction in cash flow and expected income, which is not conducive to green innovation.
TL;DR: The proposed framework restricts the spread of CO VID-19 outbreaks, ensures the safety of the healthcare teams and maintains patients' physical and psychological healthcare conditions, and is designed to deal with the severe shortage of PPE for the medical team, reduce the massive pressure on hospitals, and track recovered patients to treat COVID-19 patients with plasma.
Abstract: This paper describes a framework using disruptive technologies for COVID-19 analysis. Disruptive technologies include high-tech and emerging technologies such as AI, industry 4.0, IoT, Internet of Medical Things (IoMT), big data, virtual reality (VR), Drone technology, and Autonomous Robots, 5 G, and blockchain to offer digital transformation, research and development and service delivery. Disruptive technologies are essential for Industry 4.0 development, which can be applied to many disciplines. In this paper, we present a framework that uses disruptive technologies for COVID-19 analysis. The proposed framework restricts the spread of COVID-19 outbreaks, ensures the safety of the healthcare teams and maintains patients' physical and psychological healthcare conditions. The framework is designed to deal with the severe shortage of PPE for the medical team, reduce the massive pressure on hospitals, and track recovered patients to treat COVID-19 patients with plasma. The study provides oversight for governments on how to adopt technologies to reduce the impact of unprecedented outbreaks for COVID-19. Our work illustrates an empirical case study on the analysis of real COVID-19 patients and shows the importance of the proposed intelligent framework to limit the current outbreaks for COVID-19. The aim is to help the healthcare team make rapid decisions to treat COVID-19 patients in hospitals, home quarantine, or identifying and treating patients with typical cold or flu.
TL;DR: Wang et al. as discussed by the authors examined the impact of technological innovation on green growth, in the presence of economic growth, globalization, research & development expenditures, and human capital, with a multivariate framework in China.
Abstract: Countries around the world are making efforts to transform their industrial and economic structures in order to promote green growth, and environmentally adjusted multifactor productivity growth, that relies on cleaner and sustainable energy sources. With the Fourth Industrial Revolution coming into play, eco-friendly technologies have significantly improved and repaired the environmental conditions in modern economies. Many studies on the determining factors of green growth have attracted researchers and policymakers across the globe. However, thus far, no single study has reported the role of technological innovation, in the promotion of green growth. Therefore, this study examines the impact of technological innovation on green growth, in the presence of economic growth, globalization, research & development expenditures, and human capital between the periods of 1990 to 2018, with a multivariate framework in China. By using cointegration approaches, the results suggest that in the long-run, green growth depends on technological innovation, GDP, human capital, economic globalization, and R&D expenditures. Moreover, technological innovation is found to have a positive effect on green growth. On the policy side, any initiative that targets technological innovation, globalization, R&D, and human capital shall affect green growth. These policies should take approximately more than one year to start functioning.
TL;DR: In this paper, the authors analyzed the impact of leakages in the Middle East and North African (MENA) countries over a period of 1990-2016 and found that technological innovation has a positive impact on energy efficiency, whereas growth in shadow economy has a detrimental impact on the energy efficiency.
Abstract: Despite the ongoing research on energy efficiency and innovation in the context of Industry 4.0, little is known on how degree of leakages in economy can impact the energy efficiency-innovation association. This issue has been addressed by the United Nations in their Sustainable Development Goals (SDG) report also. In the era of Industry 4.0, this issue can be crucial from the perspective of sustainable development, and we are analyzing this issue in case of Middle East and North African (MENA) countries over a period of 1990-2016. The second-generation methodological approaches have been adopted. Our results show that technological innovation has a positive impact on energy efficiency, whereas growth in shadow economy has a detrimental impact on energy efficiency. The structural transformation of economy has positive impact on energy efficiency. Based on our results, we have designed an SDG framework, which might help the MENA countries to achieve the objectives of SDG 7, SDG 8, SDG 9, and SDG 4.
TL;DR: It is necessary to invest in adequate measures for adaptation to digital transformation, and manufacturers will end up having greater profits, productivity, and competitiveness, and from the point of view of consumers, there will be access to more and better services and greater satisfaction with the required services.
Abstract: Digital technologies are transforming the automotive industry and disrupting traditional business models. New business opportunities related to Industry 4.0 are emerging, so companies must adapt to the new environment. The study presents an application of fuzzy-set qualitative comparative analysis (fsQCA) to analyze the future impact of digital transformation on business performance models and the different actors' satisfaction. A wide range of aspects and actors derived from the digital transformation process in the automotive industry are considered. The study covers connected and autonomous driving, mobility as a service, digital information sources in car purchasing, big data, etc. The disruptive effect of the gradual introduction of electric vehicles into the market is also considered, which is boosted by environmental policies on climate change and directives for the potential use of renewable energy sources to power electric vehicles. On the other hand, the study analyses the impacts of digital transformation on the automotive industry from the point of view of different actors, ranging from automobile manufacturers, service providers, public transportation providers, and consumers to governments. The methodology has been successfully applied to a complex case study-based empirical analysis. It presents a novel application of fsQCA to digital transformation in the automotive industry in Spain. The conclusions show that it is necessary to invest in adequate measures for adaptation to digital transformation, and manufacturers will end up having greater profits, productivity, and competitiveness. From the point of view of consumers, there will be access to more and better services and greater satisfaction with the required services.
TL;DR: The study in the age of the 4th industrial revolution examines the time and frequency domain connectedness and spill-over among Fintech, green bonds, and cryptocurrencies to suggest that the total connectedness of 21st century technology assets and traditional common stocks is very high, and hence in the turbulent economy, there is a high probability of contemporaneous losses.
Abstract: The study in the age of the 4th industrial revolution examines the time and frequency domain connectedness and spill-over among Fintech, green bonds, and cryptocurrencies. Using daily data from November 2018 to June 2020, we use both DY (Diebold & Yilmaz, 2012) and BK (Barunik et al., 2017) to examine the volatility connectedness of returns series. The results of DY suggest that, first, the total connectedness of 21st century technology assets and traditional common stocks is very high, and hence in the turbulent economy, there is a high probability of contemporaneous losses. Second, Bitcoin, MSCIW, MSCI US, and KFTX are net contributors of volatility shocks whereas US dollar, oil, gold, VIX, green bond and green bond select are net receivers. Therefore, Fintech and common equities are not good hedging instruments in the same portfolio. Third, the short-term witnesses higher volatility transmission than the long-term. That is, holding assets for a long-term is likely to mitigate risks whereas trading financial assets in the short-term can increase risk because of higher volatility. Fourth, the traditional assets, gold and oil, as well as modern assets, green bonds, are useful as good hedgers compared with other assets because shock transmissions from them to Fintech, KFTX are below 0.1% and, more importantly, the total volatility spill-over of all assets in the sample is moderately average, accounting for 44.39%.
TL;DR: In this paper, the authors focus on the potential dark side of social media use among Generation Z (Gen Z) in the UK during the COVID-19 pandemic lockdown between March and May 2020, and reveal that information overload through social media had a negative impact on Gen Z social media users' psychological well-being.
Abstract: While previous research highlights the benefits of social media in times of a pandemic, this research focuses on the potential dark side of social media use among Generation Z (Gen Z) in the UK during the COVID-19 pandemic lockdown between March and May 2020. The study reveals that COVID-19 information overload through social media had a negative impact on Gen Z social media users’ psychological well-being. Moreover, perceived information overload heightened both social media fatigue and fear of COVID-19, which, in turn, increased users’ social media discontinuance intention. In addition, considering that social media is the predominant method of maintaining connectivity with others for Gen Z users during the lockdown, the fear of missing out (FoMO) buffered the impact of social media fatigue and fear of COVID-19 on Gen Z users’ social media discontinuance intention. Our research adds a hitherto underexplored perspective to the impact of the COVID-19 pandemic on young people's mental health. We offer a series of practical suggestions for social media users, social media platform providers, and health officials, institutions, and organizations in the effective and sustainable use of social media during the global COVID-19 pandemic and in the post-pandemic time.
TL;DR: In this paper, the authors developed a conceptual model to test a sample of data from 168 French hospitals using a partial least squares regression-based structural equation modeling method and found that the use of BDA-AI technologies has a significant effect on environmental process integration and green supply chain collaboration.
Abstract: Big data analytics and artificial intelligence (BDA-AI) technologies have attracted increasing interest in recent years from academics and practitioners. However, few empirical studies have investigated the benefits of BDA-AI in the supply chain integration process and its impact on environmental performance. To fill this gap, we extended the organizational information processing theory by integrating BDA-AI and positioning digital learning as a moderator of the green supply chain process. We developed a conceptual model to test a sample of data from 168 French hospitals using a partial least squares regression-based structural equation modeling method. The findings showed that the use of BDA-AI technologies has a significant effect on environmental process integration and green supply chain collaboration. The study also underlined that both environmental process integration and green supply chain collaboration have a significant impact on environmental performance. The results highlight the moderating role of green digital learning in the relationships between BDA-AI and green supply chain collaboration, a major finding that has not been highlighted in the extant literature. This article provides valuable insight for logistics/supply chain managers, helping them in mobilizing BDA-AI technologies for supporting green supply processes and enhancing environmental performance.
TL;DR: In this paper, the authors investigated the impact of Artificial Intelligence readiness on international performance and explored whether the relationship between digitalization and internationalization is affected by sustainability readiness, and found that digitalisation and sustainability are positively related, but they turn to be competing growth paths when the firm internationalizes.
Abstract: Internationalization, digitalization, and sustainability are three key growth paths for firms. In particular, the contemporary economy stresses the relevance of digital transformation as a central driver towards innovation and business renewal, especially for established small and medium sized enterprises (SMEs). However, little is known about the relationships among the above-mentioned growth paths, whether they are competing or complementary options. Our contribution addresses this gap focusing on two forms of organizational readiness within SMEs. First, it investigates the impact of Artificial Intelligence readiness on international performance. Second, it explores whether the relationship between digitalization and internationalization is affected by sustainability readiness. The empirical survey relies on a sample consisting of 438 SMEs, including both domestic and international companies. Findings confirm, as expected, that Artificial Intelligence readiness positively influences the international performance of SMEs. Moreover, we find that digitalization and sustainability are positively related, but they turn to be competing growth paths when the firm internationalizes..
TL;DR: In this paper, a conceptual model was proposed that used an integrated technology acceptance model (TAM)-TOE model and was tested using survey-based data collected from 340 employees of small, medium and large organizations.
Abstract: This study aims to identify how environmental, technological, and social factors influence the adoption of Industry 4.0 in the context of digital manufacturing. The Industry 4.0 era has brought a breakthrough in advanced technologies in fields such as nanotechnology, quantum computing, biotechnology, artificial intelligence, robotics, the Internet of Things, fifth-generation wireless technology, fully autonomous vehicles, 3D printing and so on. In this study, we attempted to identify the socioenvironmental and technological factors that influence the adoption of artificial intelligence embedded technology by digital manufacturing and production organizations. In doing so, the extended technology-organization-environment (TOE) framework is used to explore the applicability of Industry 4.0. A conceptual model was proposed that used an integrated technology acceptance model (TAM)-TOE model and was tested using survey-based data collected from 340 employees of small, medium and large organizations. The results highlight that all the relationships, except organizational readiness, organizational compatibility and partner support on perceived ease of use, were found to be significant in the context of digital manufacturing and production organizations. The results further indicated that leadership support acts as a countable factor to moderate such an adoption.
TL;DR: A decision support system for managers to predict an organization's probability of successful blockchain adoption using a machine learning technique and identifies competitor pressure, partner readiness, perceived usefulness, and perceived ease of use as the most influencing factors for blockchain adoption.
Abstract: The purpose of this paper is to provide a decision support system for managers to predict an organization's probability of successful blockchain adoption using a machine learning technique. The study conceptualizes blockchain technology as a dynamic capability that should be possessed by the organization to remain competitive. The factors influencing the blockchain adoption behavior were modeled using the theoretical lens of the Technology Acceptance Model and Technology-organisation-Environment framework. The findings identify competitor pressure, partner readiness, perceived usefulness, and perceived ease of use as the most influencing factors for blockchain adoption. A predictive decision support system was developed using a Bayesian network analysis featuring the significant factors that can be used by the decision-makers for predicting the probability of blockchain adoption in their organization. The prior probability values reported in the study may be used as indicators by the practitioners to predict their blockchain adoption probability. The practitioner will be required to substitute these probability values (high or low), as applicable to their organization to estimate the adoption probability. The use of the decision support system is likely to help the decision-makers to assess their adoption probability and develop future adoption strategies.
TL;DR: In this article, the authors identify the technologies to control the COVID-19 and future pandemics with massive data collection from users' mobile devices and discuss the important theoretical and practical implications of preserving user privacy and curbing COVID19 infections in the global public health emergency situation.
Abstract: Controlling the coronavirus pandemic is triggering a cross-border strategy by which national governments attempt to control the spread of the COVID-19 pandemic. A response based on sharing facts about millions of private movements and a call to study information behavior during the global health crisis has been advised worldwide. The present study aims to identify the technologies to control the COVID-19 and future pandemics with massive data collection from users' mobile devices. This research undertakes a Systematic Literature Review (SLR) of the studies about the currently available methods, strategies, and actions to collect and analyze data from users' mobile devices. In a total of 76 relevant studies, 13 technologies that are classified based on the following aspect of data and data management have been identified: (1) security; (2) destruction; (3) voluntary access; (4) time span; and (5) storage. In addition, in order to understand how these technologies can affect user privacy, 25 data points that these technologies could have access to if installed through mobile applications have been detected. The paper concludes with a discussion of important theoretical and practical implications of preserving user privacy and curbing COVID-19 infections in the global public health emergency situation.
TL;DR: It is shown that research opportunities are concentrated in the interfaces between the different smart dimensions, and the vision of Industry 4.0 as a concept transcending the Smart Manufacturing field, thus creating opportunities for synergies with other related fields.
Abstract: The Industry 4.0 literature has exponentially grown in the past decade. We aim to understand how this literature has evolved and propose future research opportunities. We focus on four smart dimensions of Industry 4.0: Smart Manufacturing, Smart Products and Services, Smart Supply Chain, and Smart Working. We perform a machine learning-based systematic literature review. Our analysis included 4,973 papers published from 2011 to 2020. We conducted a chronological network analysis considering the growth of these four dimensions and the connections between them. We also analyzed keywords and the main journals publishing on these four smart dimensions. We show that the literature has mainly been devoted to the study of Smart Manufacturing, although attention to the other smart dimensions has been growing in recent years. Smart Working is the less explored dimension, with many opportunities for future research. We show that research opportunities are concentrated in the interfaces between the different smart dimensions. Our findings support the vision of Industry 4.0 as a concept transcending the Smart Manufacturing field, thus creating opportunities for synergies with other related fields. Scholars can use our findings to understand the orientation of journals and gaps that can be fulfilled by future research.
TL;DR: In this paper, the authors identified and analyzed risk mitigation strategies for PFSC during the current pandemic and prioritized the identified strategies using the fuzzy-best worst methodology (F-BWM) The BWM is a highly effective decision-making method with higher consistency.
Abstract: Food Supply Chains (FSCs) are among the essential services in a pandemic Perishable food supply chains (PFSC) perform under higher risks as they struggle against greater wastage and product life cycle issues along with the logistics, operational, financial, and health risks during the COVID-19 pandemic While facing these contingencies, it is essential to formulate strategies in real-time In this paper, we identify and analyze risk mitigation strategies for PFSC during the current pandemic We have initially discussed the uncertainties and risks related to pandemic situations and subsequently identified risk mitigation strategies to manage PFSC in such situations We prioritized the identified strategies using the fuzzy-best worst methodology (F-BWM) The BWM is a highly effective decision-making method with higher consistency The fuzzy extension to the best worst method (BWM) helps in incorporating vagueness and fuzziness in the decision As a result, F-BWM is an excellent approach to analyze risk mitigation strategies as the business contingencies in PFSC during this pandemic are unique, with the industry having only a few clear ideas about how best to mitigate them Among the risk mitigation strategies, “collaborative management,” “proactive business continuity planning,” and “financial sustainability” are the top risk mitigating strategies Other identified strategies are also extremely useful for varied environmental contingencies Thus, this research has been underpinned by the contingency theory and discusses all mitigation strategies concerning the socioeconomic contingencies originating from COVID-19 This research is a novel effort in identifying and analyzing the risk mitigation strategies for enhancing the socioeconomic-ecological performance of PFSCs in meeting the sustainable development goal of healthy and safe food for everyone
TL;DR: A multilayered TOE-based risk management framework is proposed to identify and manage the risks associated with smart city governance in the current study, and the criticality of the identified risks can help researchers and practitioners understand the top risks of smart city Governance.
Abstract: Sustainable smart cities are confronted by technological, organisational and external risks, making their governance difficult and susceptible to manipulation. Based on a comprehensive literature review of 796 systematically retrieved articles, the current study proposes a multilayered technology-organisation-environment (TOE-based) risk management framework for sustainable smart city governance. A total of 56 risks are identified and grouped into TOE categories. There are 17 technological risks, including IoT networks, public internet management and user safety concerns, with a 38.7% contribution to smart city governance risks. With a 15.6% share, there are 11 organisational risks, including user data security and cloud management. There are 28 external risks with a contribution of 46.7% to the smart city governance and consist of smart city's environment, governance, integration and security risks. A multilayered TOE-based risk management framework is proposed to identify and manage the risks associated with smart city governance in the current study. The framework links smart citizens to each other through the smart city governance team and the integrated TOE layers. The iterative risk management process of identification, analysis, evaluation, monitoring and response planning is carried out in the TOE layers, both at the external layer levels and internal management levels. The proposed framework operationalises the risk management process for smart city governance by presenting the collection of pertinent risks and their thematic TOE categorisation. The criticality of the identified risks in line with the study's rankings can help researchers and practitioners understand the top risks of smart city governance. These risks present investment opportunities for city governance bodies to develop critical and effective responses as well as provide safety, security and enhanced privacy for citizens.
TL;DR: In this paper, the authors used wavelet-based quantile-on-quantile method and quantile based Granger causality method to investigate the notion of Bitcoin in actually being a safe-haven asset, amid political and economic uncertainty in the US.
Abstract: Bitcoin is considered to be an exclusive marvel of the Fourth Industrial Revolution, and is one of the most sophisticated technological and financial products. It has long been a pivot point of attention for investors who are in pursuit of a safe haven asset. In this paper, we use the wavelet-based quantile-on-quantile method, and the quantile-based Granger causality method, in order to investigate the notion of Bitcoin in actually being a safe-haven asset, amid political and economic uncertainty in the US for the period between 2010:M06, and 2020:M10. Using the Partisan Conflict Index (PCI), and the Economic Policy Uncertainty Index (EPU) as proxies of uncertainties, we find that although Bitcoin effectively appears to be a safe haven asset when uncertainties are on the rise, however, this relationship tends to change during the short- to long-run. In this regard, our sample provides us with a unique opportunity to evaluate the safe haven hypothesis for Bitcoin, amid a time span with three Presidential elections in the US, and recently, an ongoing COVID-19 outbreak, which has been declared as a global pandemic. We have also supplemented our analysis with the bootstrap rolling window causality method, as a measure of robustness. In light of the recent COVID-19 pandemic, and the dynamic economic situation, our work provides valuable knowledge for investors, who wish to construct investment portfolios based on Bitcoin, and also provide insights for regulators about how to regulate the cryptocurrency speculation in an effective manner.
TL;DR: In this paper, the use of blockchain in management has gained substantial recognition for its ability to induce transformation and innovation in existing business models and frameworks and consequently, the application of this technology to the management domain and its processes has attracted increasing interest from academia and industry.
Abstract: Blockchain has gained substantial recognition for its ability to induce transformation and innovation in existing business models and frameworks. Consequently, the application of this technology to the management domain and its processes has attracted increasing interest from academia and industry. Although research addressing the use of blockchain in management has gained momentum, this field presents a discontinuous overview of the current scope and boundary of the knowledge thereon. This study addresses this lacuna using bibliometric analyses to synthesize the prior literature. Data from Scopus 586 articles, entailing contributions from 72 countries, 273 journals, 1016 organizations, and 1284 authors, were analyzed. The findings indicate a maturing research focus on blockchain applications in specific managerial sectors, such as finance and supply chain management. However, this field's conceptual evolution is posited to be in its infancy in other sectors, such as for managing luxury goods and counterfeit products. Further, the thematic classification of the extant literature led to the identification of the following four major themes of research: strategy and regulation, enablement and implication, multi-domain deployment, and the inefficiencies of bitcoin. These findings are used to propose directions for further research in this field, such as the need for methodological advancement and theoretical grounding.
TL;DR: In this article, the authors explored the impact of corporate controversies on financial performance, and proposed the positive moderating role of ESG practices over the aforementioned relationship and found a negative and significant relationship between corporate controversies and financial performance.
Abstract: The topic of corporate social responsibility (CSR), along with the related environmental, social and governance (ESG) pillars, is playing a key role in the literature and is attracting increasing interest among managers and policymakers. Nevertheless, we still know little about how and whether corporate controversies, which are strictly related to CSR, impact firm performance. As a result, this study aims to explore the impact of corporate controversies on financial performance, and proposes the positive moderating role of ESG practices over the aforementioned relationship. Using a database of 356 European listed companies, linear regression models confirm a negative and significant relationship between corporate controversies and financial performance. However it was not possible to confirm the positive moderating effect of ESG practices on the relationship between controversies and financial performance. The study contributes to the literature on CSR and stakeholder theory, shedding light on the negative consequences of controversies and indicating that, despite no mitigating effects of ESG practices on the controversies/performance relationship have been found, ESG practices are important for addressing stakeholders’ needs. Regarding managerial implications, this study underlines that controversies are detrimental for firm performance, and that ESG practices should not serve as means for mitigating the negative effects of controversies, but rather as ways for avoiding controversies.
TL;DR: This study empirically investigated the association of BDA capability with CE performance and examined the mediating role of data-driven insights in the relationship between Bda capability and decision-making.
Abstract: Big data analytics (BDA) is a revolutionary approach for sound decision-making in organizations that can lead to remarkable changes in transforming and supporting the circular economy (CE). However, extant literature on BDA capability has paid limited attention to understanding the enabling role of data-driven insights for supporting decision-making and, consequently, enhancing CE performance. We argue that firms drive decision-making quality through data-driven insights, business intelligence and analytics (BI&A), and BDA capability. In this study, we empirically investigated the association of BDA capability with CE performance and examined the mediating role of data-driven insights in the relationship between BDA capability and decision-making. Data were collected from 109 Czech manufacturing firms, and partial least squares structural equation modeling was applied to analyze the data. The results reveal that BDA capability and BI&A are positively associated with decision-making quality. This effect is stronger when the manufacturer utilizes data-driven insights. The results demonstrate that BDA capability drives decision-making quality in organizations, and data-driven insights do not mediate this relationship. BI&A is associated with decision-making quality through data-driven insights. These findings offer important insights to managers, as they can act as a reference point for developing data-driven insights with the CE paradigm in organizations.
TL;DR: The developed instrument could help give decision makers a foundational view to measure the benefits of implementing blockchain technology before they choose to integrate it in their existing system.
Abstract: This study aims to measure the perceived business benefits of blockchain technology implementation in the banking sector and establish factors to measure these benefits. Concerns regarding security, values, and standards are essential to banking operations. Data was collected from 291 respondents who are either blockchain consultants, blockchain marketing experts, or CEOs/business heads of banks that are in the process of advising, consulting, or implementing blockchain technology. Confirmatory factor analysis (CFA) was carried out to assess the reliability and validity of the proposed instrument. The results support the proposed instrument and its five constructs. The scale emerging from this study indicates a good degree of reliability, validity and unidimensionality in each of its constructs. Technologies like blockchain are in their initial stages, and recent advances in blockchain technology may impact our findings. The developed instrument could help give decision makers a foundational view to measure the benefits of implementing blockchain technology before they choose to integrate it in their existing system. The scientific and societal significance of the study based on its practical and theoretical applications is presented at the end.